Optimization of flocculant-producing bacteria fermentation parameters by BP neural network

To optimize the fermentation parameter of Klebsiella. sp, orthogonal experiments were used to quantify the degree of the fermentation parameters of flocculant-producing bacteria MFX on the flocculation rate and the yield, which indentified temperature, shaking revolution and pH value as the input of neural network, flocculation rate and yield as the output of neural network, and then a training sample was designed. After repeat training, a prediction model with high accuracy and small error was established by which the optimum fermentation conditions: the temperature of 33 °C, the stirring speed at 141 r/min and pH value of 7.90, were obtained. Meanwhile, the actual flocculation rate and the yields were 92.67% and 2.180 9 g/L, respectively. The flocculation rate increased 4.08%, and the yields of bio-flocculant increased 14.36%, which improved the yield of bio-flocculant. The model was used to predict the fermentation process of flocculant-producing bacteria F+in the fermenter, the simulation error was small, which was the basis for the prediction and control of industrial fermentation process.